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Peiru Si,1 Jiaxing Lei,1 Chen Yang,1 Peipei Zhang,1 Xiaojiao Li,2 Shaohua Zheng,3 Qingqing Li,1 Jiye Zhang1 1School of Pharmacy, Health Science Center, Xi’an Jiaotong University, Xi’an, People’s Republic of China; 2Biobank, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China; 3Department of Anesthesiology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of ChinaCorrespondence: Qingqing Li; Jiye Zhang, School of Pharmacy, Health Science Center, Xi’an Jiaotong University, No. 76 Yanta Westroad, Xi’an, People’s Republic of China, Email liqingqing0217@xjtu.edu.cn; zjy2011@xjtu.edu.cnIntroduction: Hepatic ischemia-reperfusion injury (HIRI) is the main reason for liver dysfunction or failure after liver resection and liver transplantation. As excess accumulation of reactive oxygen species (ROS) is the leading factor, ceria nanoparticle, a cyclic reversible antioxidant, is an excellent candidate for HIRI.Methods: Manganese doped mesoporous hollow ceria nanoparticles (MnOx-CeO2 NPs) were prepared, and the physicochemical characteristics, such as particle size, morphology, microstructure, etc. were elucidated. The in vivo safety and liver targeting effect were examined after i.v. injection. The anti-HIRI was determined by a mouse HIRI model.Results: MnOx-CeO2 NPs with 0.40% Mn doped exhibited the strongest ROS-scavenging capability, which may due to the increased specific surface area and surface oxygen concentration. The nanoparticles accumulated in the liver after i.v. injection and exhibited good biocompatibility. In the HIRI mice model, MnOx-CeO2 NPs significantly reduced the serum ALT and AST level, decreased the MDA level and increased the SOD level in the liver, prevent pathological damages in the liver.Conclusion: MnOx-CeO2 NPs were successfully prepared and it could significantly inhibit the HIRI after i.v. injection.Graphical Abstract: Keywords: hepatic ischemia reperfusion injury, Mn-doped mesoporous hollow cerium oxide nanoparticles, oxidative stress

IntroductionAs a source of low-cost and high-quality meat for human beings, the consumption of camel meat was increasing, and beef has similar texture and nutritional characteristics with camel meat. Camel hump and fatty-tails are important parts of fat storage for camels and fat-tailed lambs, respectively, which were to adapt and endure harsh environments. Considering their similar physiological functions, their fat composition might be similar. Lipidomics is a system-level analysis of lipids method, which play an important role in the determination and quantification of individual lipid molecular specie, food adulteration and labeling.MethodsA GC/MS was used to analyze fatty acids composition of Xinjiang Bactrian camel meat, hump, beef, and fatty-tails. UPLC-Q-TOF/MS based on lipidomics approach was used to analyze lipid composition, characterize and examine the lipid differences in Xinjiang Bactrian camel meat, hump, beef, and fatty-tails.Results and discussionThe major fatty acids of the four samples were C16:0, C18:0, and C18:1cis, and camel meat had a significant low SFA content and high MUFA content. A total of 342 lipid species were detected, 192, 64, and 79 distinguishing lipids were found in the groups camel hump compared to camel meat, camel meat compared to beef, and camel hump compared to fatty-tails, respectively. Lipid metabolisms of ether lipid, glycerophospholipid, glycerolipid, and sphingolipid were the most influential pathways revealed by KEGG analysis. The results contributed to enrich the lipid information of Bactrian camel meat, and indicated that UPLC-Q-TOF/MS based on lipidomics was an alternative method to distinguish meat samples.

BackgroundTreatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.MethodsBased on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.ResultsBased on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.ConclusionsPMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.

Supplementary Material 1

Additional file 1: Supplementary Fig. 1 m6A gene expression between UCEC patients with or without mutations in m6A regulators. *, P < 0.05; **, P < 0.01.

Supplementary Material 2

Background: Informative gene selection is an essential step in performing tumor classification. However, it is difficult to select informative genes related to tumors from large-scale gene expression profiles because of their characteristics, such as high dimensionality, relatively small samples, and class imbalance, and some genes are superfluous and irrelevant. Objective: Many researchers analyze and process gene expression data to obtain classified gene subsets by using machine learning methods. However, the gene expression profiles of tumors often have massive computational challenges. In addition, when improving feature importance and classification accuracy, cost estimation is often ignored in traditional feature selection algorithms, which makes tumor classification more difficult. Methods: In this study, a novel informative gene selection method based on cost-sensitive fast correlation- based filter (CS-FCBF) feature selection is proposed. Results: First, the symmetric uncertainty index is used to evaluate the correlation between informativeFirst, the symmetric uncertainty index is used to evaluate the correlation between informative genes and class labels, then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, costsensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. genes and class labels, then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, costsensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. Conclusion: This experiment was verified in eight independent tumor datasets. By comparing and analyzing CS-FCBF with another three hybrids of typical gene selection algorithms combined with cost-sensitive learning, we found that the method proposed in this study has a better classification performance with fewer selected genes, which might provide guidance in tumor diagnosis and research.

Corneal neovasularization (CNV) is one of the most important causes of visual impairment worldwide. Dasatinib, a poorly water-soluble tyrosine kinase inhibitor with dual Src family kinase and platelet derived growth factor receptor inhibiting capability, has a great potential in the treatment of CNV. In this study, dasatinib was successfully encapsulated into a nanostructured lipid carrier (dasa-NLC) and the size was approximately 78 nm with a small polydispersity index. The NLC increased the solubility of dasatinib by more than 1220 times, sustained the drug release, reduced the ocular toxicity and facilitated its penetration into the cornea. Dasa-NLC significantly inhibited the proliferation, migration and tube formation of HUVEC cells, the three most important angiogenesis-related cellular changes of the CNV. Next, the in vivo anti-CNV effect of dasa-NLC was evaluated by using an alkaline burned mice CNV model, in which the development of the CNV and pathological changes of the cornea were significantly inhibited. The immunohistochemistry analysis indicated that dasa-NLC could inhibit both the expression and activation of Src family kinase. Therefore, dasa-NLC showed considerable promise in the treatment of CNV.

Wind observations from a multi-level observation tower are used to estimate the dissipative heating (DH) in three landfalling typhoons. The observations reveal the presence of a stress internal boundary layer (IBL) in the typhoon circulations. The integrated DH values increase with increasing wind speed above the IBL. The integrated DH calculated with the turbulent spectra method increases more rapidly with increasing wind speed than that estimated with the theoretical method. However, the integrated DH estimated using the theoretical method is significantly smaller than that calculated with the turbulent spectra method because dissipative rates with the turbulent spectra method are much larger above the IBL.

Numerous findings from functional neuroimaging research suggest that overweight may be associated with alterations in reactive inhibition. However, there is a dearth of research investigating the functional connectivity that mediates intentional inhibition in overweight individuals. To explore this issue, 55 overweight and 45 normal-weight adults completed an assessment consisting of a resting-state functional magnetic resonance imaging scan, a behavioural task measuring food-specific intentional inhibition, and a 1-year longitudinal measurement of BMI change. A seed-based approach was employed to examine the group-difference of the resting-state functional connectivity (rsFC) of the medial frontal cortex (MFC) (dorsal fronto-medial cortex [dFMC], pre-supplementary motor area, and premotor cortex) regions involved in intentional inhibition. Compared with normal-weight adults, the overweight individuals exhibited higher rsFC between the MFC seeds and (i) cerebellum, (ii) postcentral gyrus, (iii) middle temporal gyrus, and (iv) posterior cingulate cortex, while lower rsFC strength were observed between MFC seeds and (i) putamen and (ii) insula. The overweight individuals with higher dFMC-cerebellum rsFC strength showed poorer performance in food-specific intentional inhibition and gained more weight a year later than those of normal-weight participants. Results suggested that altered functional connections between MFC and regions associated with reward and maladaptive eating may be key neural mechanisms of food-specific intentional inhibition in overweight status. Therefore, individuals are encouraged to make informed decisions about their health and reduce their consumption of obesogenic foods from the perspective of intentional inhibition.

Additional file 1: Fig. S1. Hematoxylin eosin (H&E) staining of samples in this study. Fig. S2. Patient information and quality control of single-cell RNA-seq data. Fig. S3. Transcriptome analysis of different cancer subtypes. Fig. S4. Mixed-lineage cancer cells identification at single-cell transcriptome level and at protein level. Fig. S5. Copy number variation analysis. Fig. S6. Pseudotime analysis revealed transcriptome dynamics during tumorigenesis. Fig. S7. Knockdown and inhibition of AKR1B1 decreased tumor cell growth.

Additional file 1: Figure S1. Identify of mBMSCs. Figure S2. Exo-PBS did not prevent the progress of STOA.

Additional file 1: Figure S1. The PR (precision-recall) curves of (a) Optimized XGBoost, (b) XGBoost, (c) support vector machine, (d) decision tree, (e) K-nearest neighbor, (f) logistic regression, and (g) random forest binary classifiers.

The rapid development of modern information technology needs highly thermal-conductive with high-frequency copper clad laminates (HFCCLs) as a key point. In this study, ShBN (spherical hexagonal boron nitride) and ShBN/KH550 (surface modified by KH550) were used as fillers to enhance the thermal conductivity for the insulating layer of HFCCLs. Due to the merits of not only the rotational symmetry for effective thermal networks, but also a compatibility with the polymer matrix, the ShBN/KH550 filled with HFCCLs (SK-CCLs) could reach a high value of 1.18 W m−1K−1 when the content of the filler was 63 wt%. On the other hand, the incorporation of ShBN and ShBN/KH550 fillers had led to the decrease the dielectric losses with a low dielectric permittivity even at a high testing frequency of 5 GHz. In addition, the peel strength and flexural strength of the HFCCLs were not significantly reduced with the addition of either ShBN or ShBN-KH550 fillers, showing the same downward trend. This study presents a new direction to promote the conductive insulating layer for HFCCLs to meet the high requirement of upcoming 5G or 6G era.

Obtaining high-precision wind speed prediction results is very beneficial to the utilization of wind energy and the operation of the power system. The purpose of this study is to develop a novel model for wind speed causality processing and short-term wind speed forecasting. In this study, the hybrid model combining causality processing strategy called “decomposition- virtual nodes-pruning” and Long Short-Term Memory Network based on Neighborhood Gates is proposed to obtain high-precision wind speed predictions. First, Pearson Correlation Coefficient, Maximal Information Coefficient and Granger causality test are used to explore the correlation and causality between wind speed and meteorological factors. Then, the causality is divided into five categories: center, chained, ring, tree and network causality, according to the topological structure of causality. Next, all types of causality can be unified into an equivalent tree causality by the causality processing strategy. Afterward, Long Short-Term Memory Network based on Neighborhood Gates is proposed to dynamically adjust the network structure according to the specific equivalent tree causality. Finally, the performance of the proposed model is verified by eight models from three aspects with different features, different methods and different equivalent trees in the case in Fuyun meteorological station, Xinjiang province, China. The evaluation metrics of the prediction results obtained by the proposed model are optimal among the eight models. The experimental results show that the proposed model is very competitive and very suitable for processing complex causality in wind speed prediction.

The traditional synthetic aperture radar (SAR) platform positioning methods based on the range-Doppler domain data, velocity and altitude information of the platform exist a large approximation error, which will not satisfy the requirement of accurate navigation. Additionally, the traditional positioning methods for solving non-linear equations are challenging due to the risk of falling into local optimal solution. In order to solve these problems, a new robust SAR platform positioning method based on the improved Gauss–Newton–genetic hybrid (GNGH) algorithm is proposed in this study. First, the position information of multi-feature points in geographic and imaging coordinate systems is acquired by adopting the image matching. Furthermore, the coordinate transformation is introduced to realise the uniform of feature point coordinates between the different coordinate systems. Finally, the accurate position of the platform is calculated through introducing GNGH algorithm to solve the non-linear equations constructed by the corresponding slant range information. So the trajectory deviation can be corrected effectively. Moreover, the proposed method can reduce the computation load and positioning error dramatically. The effectiveness of the proposed approach is confirmed and demonstrated via simulation and experimental results.

Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an increasing demand for aerial data. Accurate navigation and localization, required for precise data collection in many industrial applications, often relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a setup and calibration process and are relatively expensive. This paper addresses the problem of accurate positioning and navigation of UAVs through cooperative localization. Inexpensive ultra-wideband (UWB) transceivers installed on both the UAV and a support ground robot enable centimeter-level relative positioning. With fast deployment and wide setup flexibility, the proposed system is able to accommodate different environments and can also be utilized in GNSS-denied environments. Through extensive simulations and test fields, we evaluate the accuracy of the system and compare it to GNSS in urban environments where multipath transmission degrades accuracy. For completeness, we include visual-inertial odometry in the experiments and compare the performance with the UWB-based cooperative localization.

Ultra-wideband (UWB) technology is a mature technology that contested other wireless technologies in the advent of the IoT but did not achieve the same levels of widespread adoption. In recent years, however, with its potential as a wireless ranging and localization solution, it has regained momentum. Within the robotics field, UWB positioning systems are being increasingly adopted for localizing autonomous ground or aerial robots. In the Industrial IoT (IIoT) domain, its potential for ad-hoc networking and simultaneous positioning is also being explored. This survey overviews the state-of-the-art in UWB networking and localization for robotic and autonomous systems. We also cover novel techniques focusing on more scalable systems, collaborative approaches to localization, ad-hoc networking, and solutions involving machine learning to improve accuracy. This is, to the best of our knowledge, the first survey to put together the robotics and IIoT perspectives and to emphasize novel ranging and positioning modalities. We complete the survey with a discussion on current trends and open research problems., Comment: 2021 10th Mediterranean Conference on Embedded Computing, Track: CPS&IoT

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X211038138 for MRI-visible perivascular spaces in basal ganglia but not centrum semiovale or hippocampus were related to deep medullary veins changes by Kemeng Zhang, Ying Zhou, Wenhua Zhang, Qingqing Li, Jianzhong Sun and Min Lou in Journal of Cerebral Blood Flow & Metabolism

Depression, a major mental illness, has widely affected lives all over the world. Being depressed not only affects patients’ mood, but also has a negative impact on patients’ physical and mental health. It may lead to the lack of enthusiasm for daily life, low mental state, anxiety, irritability, anger and even suicidal tendencies. As the need to automatically detect depression using machine learning algorithms increases, an automatic depression detection method based on audio files and convolutional neural network (CNN) is proposed in this paper. First of all, we delete long silent sections of each audio file and splice the rest into a brand-new one. After that, add the label which represents if the participant is healthy or not to each file. Then, Mel frequency cepstrum coefficients (MFCCs), the features of speech signal, are extracted into matrix vector feature to represent the particular characteristics of participants’ own voice. Eventually, the features are imported into the convolutional neural network model to complete the model training and evaluation. The results on Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset show that the overall prediction accuracy is 0.85, and the average probability of correct prediction of a single file is 0.82.

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X211038138 for MRI-visible perivascular spaces in basal ganglia but not centrum semiovale or hippocampus were related to deep medullary veins changes by Kemeng Zhang, Ying Zhou, Wenhua Zhang, Qingqing Li, Jianzhong Sun and Min Lou in Journal of Cerebral Blood Flow & Metabolism

Supplemental material, sj-docx-2-pec-10.1177_03010066211052943 for Semantically Congruent Bimodal Presentation with Divided-Modality Attention Accelerates Unisensory Working Memory Retrieval by Hongtao Yu*, Aijun Wang*, Qingqing Li, Yulong Liu, Jiajia Yang, Satoshi Takahashi, Yoshimichi Ejima, Ming Zhang and Jinglong Wu in Perception

Supplemental material, sj-docx-2-pec-10.1177_03010066211052943 for Semantically Congruent Bimodal Presentation with Divided-Modality Attention Accelerates Unisensory Working Memory Retrieval by Hongtao Yu*, Aijun Wang*, Qingqing Li, Yulong Liu, Jiajia Yang, Satoshi Takahashi, Yoshimichi Ejima, Ming Zhang and Jinglong Wu in Perception

Supplemental material, sj-pdf-1-pec-10.1177_03010066211052943 for Semantically Congruent Bimodal Presentation with Divided-Modality Attention Accelerates Unisensory Working Memory Retrieval by Hongtao Yu*, Aijun Wang*, Qingqing Li, Yulong Liu, Jiajia Yang, Satoshi Takahashi, Yoshimichi Ejima, Ming Zhang and Jinglong Wu in Perception

Additional file 1: Figure S1. Particle size distribution of Cur@α-NTP-LNs. Dynamic light scattering (DLS) measurement showing the intensity weighted particle size of Cur@α-NTP-LNs that varied from 15.2 to 119.8 nm with the increasing amount of α-NTP peptide. Figure S2. Excitation and emission spectra of free curcumin in PBS solution at room temperature. Figure S3. Inhibition of the proliferation of 5-8F cells treated with PBS, 50 µM free Cur, Cur equivalent Cur@α-NTP-LNs, or peptide equivalent α-NTP-LNs (100 µM) at 24 h. Scale bar = 100 µm. Figure S4. Semi-native SDS-PAGE assay and fluorescence imaging to evaluate the stability of Cur(DiR-BOA)@α-NTP-LNs. Figure S5. Anti-cancer effects of Cur@α-NTP-LNs on mRFP-5-8F lung metastasis. (a) Overlay of bright field and color fluorescence images showing mRFP-5-8F tumor metastasis that occurred in free Cur-treated mice but undetectable in both Cur@α-NTP-LNs- and α-NTP-LNs-treated mice by necropsy. (b) Fluorescence imaging of lung tissues collected from mice in a. (c) Histopathological analyses of H&E-stained lung tissues from mice treated with 135 nmol free Cur, Cur equivalent Cur@α-NTP-LNs or peptide equivalent α-NTP-LNs at day 35 after mRFP-5-8F cell injection. Scale bar = 30 µm.

Supplemental material, sj-pdf-1-pec-10.1177_03010066211052943 for Semantically Congruent Bimodal Presentation with Divided-Modality Attention Accelerates Unisensory Working Memory Retrieval by Hongtao Yu*, Aijun Wang*, Qingqing Li, Yulong Liu, Jiajia Yang, Satoshi Takahashi, Yoshimichi Ejima, Ming Zhang and Jinglong Wu in Perception

The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so-called last-mile delivery occurs. In these scenarios, local motion estimation is combined with the matching of real-time data with a detailed pre-built map. In this paper, we utilize data gathered with an autonomous delivery robot to compare different sensor fusion techniques and evaluate which are the algorithms providing the highest accuracy depending on the environment. The techniques we analyze and propose in this paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder readings. We show how lidar scan matching combined with other sensor data can be used to increase the accuracy of the robot localization and, in consequence, its navigation. Moreover, we propose a strategy to reduce the impact on navigation performance when a change in the environment renders map data invalid or part of the available map is corrupted.

Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods against adversarial agents in distributed learning, among many others. In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous. These variations can happen due to sensing mismatches, inherent errors in terms of calibration of the mechanical joints, or simple differences in accuracy. While our results are simulation-based, we introduce the effect of sensing, calibration, and accuracy mismatches in distributed reinforcement learning with proximal policy optimization (PPO). We discuss on how both the different types of perturbances and how the number of agents experiencing those perturbances affect the collaborative learning effort. The simulations are carried out using a Kuka arm model in the Bullet physics engine. This is, to the best of our knowledge, the first work exploring the limitations of PPO in multi-robot systems when considering that different robots might be exposed to different environments where their sensors or actuators have induced errors. With the conclusions of this work, we set the initial point for future work on designing and developing methods to achieve robust reinforcement learning on the presence of real-world perturbances that might differ within a multi-robot system., Accepted to the 5th International Conference on Robotics and Automation Engineering, IEEE, 2020

Unmanned Aerial Vehicles (UAVs) have been playing an increasingly active role in supporting search and rescue (SAR) operations in recent years. The benefits are multiple such as enhanced situational awareness, status assessment, or mapping of the operational area through aerial imagery. Most of these application scenarios require the UAVs to cover a certain area. If the objective is to detect people or other objects, or analyze in detail the area, then there is a trade-off between speed (higher altitude coverage) and perception accuracy (lower altitude). An optimal point in between requires active perception on-board the UAV to dynamically adjust the flight altitude and path planning. As an initial step towards active vision in UAV search in maritime SAR scenarios, in this paper we focus on analyzing how the flight altitude affects the accuracy of object detection algorithms. In particular, we quantify what are the probabilities for false negatives and false positives in human detection at different altitudes. Our results define the correlation between the altitude and the ability of UAVs to effectively detect people in the water. acceptedVersion

Mobile communication base stations are the basic facilities of telecommunication operation networks. When the communication base station is struck by lightning, a very high overvoltage will appear on the top of pole, which leads the power supply cable of RRU(Remote Radio Unit) directly break through, and a large lightning current will flow through the cable, damaging the communication equipment and the power supply. Therefore, the research on the lightning current distribution characteristics of the mobile communication base station has important theoretical significance and engineering application value for the lightning protection of the mobile communication base station. In this paper, a simulation model of the current distribution characteristics of the communication pole system with overvoltage protective varistor is established, and the influence of the impulse current, the length of the pole and the DC reference voltage on the current distribution characteristics of the pole system is studied. The results show that: (1) When $8 /20\ \mu \mathrm{s}$ impulse current is injected into the 10m long pole system, the dynamic resistance $r$ of varistor decreases with the increase of impulse current peak value, the current distribution ratio of pole and core of power cable gradually approaches to 9:1. (2) As the length of the pole increases, the influence of the varistor on the current waveform parameters of the core branch of the power gradually decreases, and the front time distortion rate through the power line core branch decreases. (3) When the peak value of the impulse current and the length of the pole are the same, the lower the DC reference voltage of the varistor, the smaller the current waveform distortion of the core branch of the power, and the greater the current distribution.

Autonomous small robots are getting a lot of attention for their use in industrial and domestic purposes. These are equipped with few to numerous amounts of sensors to help understand the operating environment, take appropriate next-step decisions, prioritize actions and operate with little to no human intervention. Light detection and ranging (LIDAR) sensors are widely used for detecting objects around an autonomous robot and to drive by effectively avoiding any possible obstacle. However, cheap LIDARs available in the market mostly can measure the distance of surrounding objects with a rotating LASER beam and an accompanying sensor, thus obtaining a two-dimensional (2D) map. This is insufficient for extracting enough contextual data for autonomous robot movement. In this paper, we propose a novel approach for enhancing the detection capabilities of a typical 2D LIDAR to yield perception in three dimensions (3D). This can facilitate more efficient object detection while keeping the costs of small-robots to an affordable level. The proposed approach requires little customization to achieve reasonably enhanced mapping. From our experiments, it is evident that our proposed method provides better mapping and detection capabilities using commonly available low-cost 2D LIDAR. Furthermore, integration of Edge computing makes data processing efficient and noticeably increases battery life of a robot resulting in a longer run-time.

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles., Accepted to the Fifth International Conference on Fog and Mobile Edge Computing (FMEC 2020)

Ulvan extracted from the green seaweed Ulva was added in laying hen diets. The effect of ulvan on laying performance, egg quality, immunity function, and antioxidant capacity was evaluated using Hy-Line Brown hens. Six groups of birds (n = 864, 61 weeks old) were fed on the basal diet containing ulvan at 0, 0.05, 0.1, 0.5, 0.8, and 1% for 8 weeks. The results were compared with the control group. Ulvan at concentrations of 0.1 to 1% can significantly improve the egg production (P

An increase in the cache capacity is usually accompanied by a decrease in access speed. To balance the capacity and performance of caches, this paper proposes an instruction cache (ICache) architecture based on divide-by-2 memory banks (D2MB-ICache). The control circuit and memory banks of D2MB-ICache work at the central processing unit (CPU) frequency and the divide-by-2 CPU frequency, respectively, so that the capacity of D2MB-ICache can be expanded without lowering its frequency. For sequential access, D2MB-ICache can output the required instruction from memory banks per CPU cycle by dividing the memory banks with a partition mechanism and employing an inversed clock technique. For non-sequential access, D2MB-ICache will fetch certain jump instructions one or two more times, so that it can catch the jump of the request address in time and send the correct instruction to the pipeline. Experimental results show that, compared with conventional ICache, D2MB-ICaches with the same and double capacities show a maximum frequency increase by an average of 14.6% and 6.8%, and a performance improvement by an average of 10.3% and 3.8%, respectively. Moreover, the energy efficiency of 64-kB D2MB-ICache is improved by 24.3%.

Recently, using quorum-sensing inhibitors to degrade signaling molecules and mitigating membrane's biofouling is a new breakthrough. In this study, a new graphene oxide@acylase (GO@AC) nanoparticle was prepared via adsorption method. GO, GO@AC and Polyvinyl pyrrolidone (PVP) were added to a poly(vinylidene fluoride) (PVDF) membrane casting solution by embedding and blending, respectively, and the nanohybrid membranes were prepared through phase inversion by the immersion precipitation technique. The results showed that the biological activity of PVDF/GO@AC/PVP membrane could be maintained for about 28–30 days. To examine the antibiofouling characteristics of nanohybrid membranes, sterilized nanohybrid membranes were immersed in a Pseudomonas aeruginosa nutrient solution for 120 h, and the three-dimensional structures of biofilm were observed by a confocal laser scanning microscope. The results showed that the biofilm thickness of PVDF/PVP, PVDF/GO/PVP and PVDF/GO@AC/PVP membrane was about 60, 25 and 40 µm, respectively; the biomass proportion of the latter membrane was 3.04 µm 3 /μm 2 , which was only 16% of the PVDF membrane biomass. This showed that the new PVDF/GO@AC/PVP membrane had better inhibitory effect on biofilm formation, and this study provided a new way to mitigate the membrane's biofouling.

Clinical evidence suggests that there exists a strong correlation between Zika virus (ZIKV) infection and abnormal development of the nervous system. However, the underlying mechanisms remain to be elusive. In this study, recombinant lentiviral vectors coding for ZIKV structural proteins and truncations (prM-Env, M-Env and Env) were transduced into PC12 cells. Envelope (Env) overexpression induced significant inhibition of proliferation and triggered G2/M cell cycle arrest and apoptosis in PC12 cells. Flow cytometry and western blot analysis showed that the apoptosis was associated with up-regulation of both p53 and p21Cip1/Waf1 and down-regulation of cyclin B1. Presence of aberrant nuclei clusters were confirmed by immunofluorescence staining analysis. The data indicate that overexpression of prM-Env, M-Env or Env led to apoptosis via an intrinsic cell death signaling pathway that is dependent on the activation of caspase-9 and caspase-3 and accompanied by an increased ratio of Bax to Bcl-2 in transduced PC12 cells. In summary, our results suggest that ZIKV Env protein causes apoptosis in PC12 cells via an intrinsic cell death signaling pathway, which may contribute to ZIKV-induced abnormal development of the nervous system.

Ag/AgBr/AgCl hollow microspheres were synthesised through template assisted synthesis method followed by calcination and light reduction method. Field emission scanning electron microscopy, UV–Vis absorption spectra and X-ray diffraction were utilized to characterize the morphology, optical absorption property and phase structure of the as-prepared photocatalyst, respectively. By degradation of Rhodamine B under visible light, the Ag/AgBr/AgCl hollow microspheres exhibited a significant enhancement in photocatalytic activity compared with Ag/AgCl and Ag/AgBr. Furthermore, the heterojunction formed between AgCl and AgBr, the remarkable visible light absorption attributed to Ag nanoparticles on the surface of AgBr are considered as the main reasons of the enhancement.

Graphene oxide (GO) and lithium chloride (LiCl) were introduced into poly (vinylidene fluoride) (PVDF) membranes prepared using the immersion precipitation technique. The hydrophilicity, water flux, X-ray diffraction (XRD), zeta potential, SEM and AFM images of membranes were investigated. By adjusting the content of GO from 0 wt% to 0.9 wt%, the water flux of PVDF/GO/LiCl nanohybrid membranes varied from 48.4 L/m2 h to 61.90 L/m2 h at 100 kPa. Because of the synergistic effects of GO and LiCl particles, the surface free energy (| Δ G s |) of M3 (0.5 wt% GO) membrane was highest (103.50 mJ/m2), indicating that the hydrophilicity of M3 membrane was highest. Rhodamine B aqueous solution was firstly used to determine the adsorption and desorption ability of membranes. After 20 cycles, the decolorization rates and flux recovery ratios of nanohybrid membranes exceeded 80% and 78.2% respectively, and the Rhodamine B rejection of M3 membrane was maximum (67.8%) at 100 kPa, which illustrated that nanohybrid membranes can be reused as a better ultrafiltration and dye recovery material, and the characteristic of M3 membrane was best.

Recently, much interest has been paid to the separation of silk produced by Bombyx mori from silk produced by other species and tracing the beginnings of silk cultivation from wild silk exploitation. In this paper, significant differences between silks from Bombyx mori and other species were found by microscopy and spectroscopy, such as morphology, secondary structure, and amino acid composition. For further accurate identification, a diagnostic antibody was designed by comparing the peptide sequences of silks produced by Bombyx mori and other species. The results of the noncompetitive indirect enzyme-linked immunosorbent assay (ELISA) indicated that the antibody that showed good sensitivity and high specificity can definitely discern silk produced by Bombyx mori from silk produced by wild species. Thus, the antibody-based immunoassay has the potential to be a powerful tool for tracing the beginnings of silk cultivation. In addition, combining the sensitive, specific, and convenient ELISA technology with other conventional methods can provide more in-depth and accurate information for species identification.

Novel high-flux and antifouling membranes were prepared by introducing the “three-dimensional structure” nanohybrid particle (GO@SiO2) and polyvinylpyrrolidone (PVP) into polyvinylidene fluoride (PVDF) casting solution. GO@SiO2 nanohybrid particle was produced via in situ hydrolysis and condensation of precursor tetraethoxysilane (TEOS) on the surfaces of graphene oxide (GO) nanosheets, and the nanohybrid membranes were first prepared through phase inversion by an immersion precipitation technique. Compared with PVDF/PVP, PVDF/SiO2/PVP and PVDF/GO/PVP membranes, PVDF/GO@SiO2/PVP membrane had outstanding performance in hydrophilicity, surface roughness, and mechanical force. The rejection rate of bovine serum albumin (BSA) exceeded 78.5% under high water flux (1232 L/(m2 h)) and the membrane flux recovery rate was highest (BSA = 77.5%, sodium alginate (SA) = 82.1%). To investigate the antifouling properties of the nanohybrid membrane further, the adhesion forces of foulant and membrane were determined using our own foulant probes with an atomic force microscope (AFM). The results showed that the adhesion forces between PVDF/GO@SiO2/PVP membrane and foulants (BSA and SA) were far smaller than the adhesive forces of the foulants with the PVDF/PVP membrane. The results showed that the new nanohybrid membrane had the best overall properties and antifouling ability, and it provides a new method for improving the performance of PVDF membranes.

In recent years, the availability of reverse genetics systems for porcine reproductive and respiratory syndrome virus (PRRSV) has created new perspectives for the use of recombinant viruses as expression vectors. Most of these recombinant PRRSV vectors express foreign genes through either an independent transcription unit inserted in ORF1b and ORF2, or in ORF7 and the 3' UTR. The aim of this study was to find an alternative site for foreign gene insertion into the PRRSV genome. Here, we constructed an infectious cDNA clone for a cell-adapted PRRSV strain, GXNN1396-P96. This cDNA-clone-derived recombinant virus (rGXAM) was comparable in its growth kinetics in MARC-145 cells to the parental virus, GX1396-P96. Using the infectious cDNA-clone, we inserted an independent transcription unit in ORF4 and ORF5a to generate a novel PRRSV-based recombinant virus expressing the green fluorescent protein (GFP) gene. Biological characterization of the recombinant virus, rGX45BSTRS-GFP, showed that it maintained similar growth characteristics but produced fewer infectious virions than the parental PRRSV. These data demonstrate that the ORF4 and ORF5a site is able to tolerate the insertion of foreign genes.

Fleets of autonomous mobile robots are becoming ubiquitous in industrial environments such as logistic warehouses. This ubiquity has led in the Internet of Things field towards more distributed network architectures, which have crystallized under the rising edge and fog computing paradigms. In this paper, we propose the combination of an edge computing approach with computational offloading for mobile robot navigation. As smaller and relatively simpler robots become more capable, their penetration in different domains rises. These large multi-robot systems are often characterized by constrained computational and sensing resources. An efficient computational offloading scheme has the potential to bring multiple operational enhancements. However, with the most cost-effective autonomous navigation method being visual-inertial odometry, streaming high-quality images can induce latency increments with a consequent negative impact on operational performance. In this paper, we analyze the impact that image quality and compression have on the state-of-the-art on visual inertial odometry. Our results indicate that over one order of magnitude in image size and network bandwidth can be reduced without compromising the accuracy of the odometry methods even in challenging environments. This opens the door to further optimization by dynamically assessing the trade-off between image quality, network load, latency and performance of the visual-inertial odometry and localization accuracy.

A recent trend in the IoT is to shift from traditional cloud-centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as realtime analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system's latency and poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multivehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.

The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression are run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications.

Host−guest interactions are of central importance in many biological and chemical processes. However, the investigation of the formation and decomplexation of host−guest systems at the single-molecule level has been a challenging task. Here we show that the single-molecule conductance of organoplatinum(II) metallocycle hosts can be enhanced by an order of magnitude by the incorporation of a C60 guest molecule. Mechanically stretching the metallocycle-C60 junction with a scanning tunneling microscopy break junction technique causes the release of the C60 guest from the metallocycle, and consequently the conductance switches back to the free-host level. Metallocycle hosts with different shapes and cavity sizes show different degrees of flexibility to accommodate the C60 guest in response to mechanical stretching. DFT calculations provide further insights into the electronic structures and charge transport properties of the molecular junctions based on metallocycles and the metallocycle-C60 complexes. &nbsp

Three-dimensional representations and maps are the key behind self-driving vehicles and many types of advanced autonomous robots. Localization and mapping algorithms can achieve much higher levels of accuracy with dense 3D point clouds. However, the cost of a multiple-channel three-dimensional lidar with a 360°field of view is at least ten times the cost of an equivalent single-channel two-dimensional lidar. Therefore, while 3D lidars have become an essential component of self-driving vehicles, their cost has limited their integration and penetration within smaller robots. We present an FPGA-based 3D lidar built with multiple inexpensive RPLidar A1 2D lidars, which are rotated via a servo motor and their signals combined with an FPGA board. A C++ package for the Robot Operating System (ROS) has been written, which publishes a 3D point cloud. The mapping of points from the two-dimensional lidar output to the three-dimensional point cloud is done at the FPGA level, as well as continuous calibration of the motor speed and lidar orientation based on a built-in landmark recognition. This inexpensive design opens a wider range of possibilities for lower-end and smaller autonomous robots, which can be able to produce three-dimensional world representations. We demonstrate the possibilities of our design by mapping different environments.

The development of autonomous vehicles has seen considerable advances over the past decade. However, specific challenges remain in the area of autonomous waterborne navigation. Two key aspects in autonomous surface vehicles are sensor calibration and segmentation of water surface. Cameras and other sensors in a car or drone can be installed accurately in a specific position and orientation. In a large vessel, this is not always possible, as sensors might be installed around the vessel or on masts. Taking advantage of the medium in which these vehicles operate, the water plane can be used as a reference for different sensors to calibrate their orientation. This allows more accurate localization of obstacles of objects. State-of-the-art deep learning techniques have been successfully applied for water surface segmentation in open sea. However, in other environments such as small rivers or lakes with still waters, a different approach might enable more accurate water surface estimation. We propose a method to estimate the water plane based on the detection of local symmetry planes that naturally occur when objects are reflected in the water. By using a point cloud generated with a stereo camera, we are able to accurately estimate the water level and, at the same time, calibrate the camera position and improve the localization of obstacles. We assume that an approximate position and orientation of the camera is known with respect to the sea level. We demonstrate the efficiency of our method with data obtained in the Aura river, Finland, with our prototype vessel facing both the riverside and the center of the river.